This paper studies indoor localisation problem by using low-cost and pervasive sensors. Most of existing indoor localisation algorithms rely on camera, laser scanner, floor plan or other pre-installed infrastructure to achieve submeter or sub-centimetre localisation accuracy. However, in some circumstances these required devices or information may be unavailable or too expensive in terms of cost or deployment. This paper presents a novel keyframe based Pose Graph Simultaneous Localisation and Mapping (SLAM) method, which correlates ambient geomagnetic field with motion pattern and employs low-cost sensors commonly equipped in mobile devices, to provide positioning in both unknown and known environments. Extensive experiments are conducted in largescale indoor environments to verify that the proposed method can achieve high localisation accuracy similar to state-of-thearts, such as vision based Google Project Tango.
